Florida International UniversityFIU Digital Commons

Department of Physics College of Arts, Sciences & Education

8-29-2016

Multi-Frequency Blazar Micro-Variability as a Toolto Investigate Relativistic JetsJames R. WebbDepartment of Physics, Florida International University, webbj@fiu.edu

Follow this and additional works at: https://digitalcommons.fiu.edu/physics_fac

Part of the Physics Commons

This work is brought to you for free and open access by the College of Arts, Sciences & Education at FIU Digital Commons. It has been accepted forinclusion in Department of Physics by an authorized administrator of FIU Digital Commons. For more information, please contact dcc@fiu.edu.

Recommended CitationWebb, J.R. Multi-Frequency Blazar Micro-Variability as a Tool to Investigate Relativistic Jets. Galaxies 2016, 4, 15

galaxies

Article

Multi-Frequency Blazar Micro-Variability as a Tool toInvestigate Relativistic Jets

James R. Webb

Department of Physics, Florida International University, Miami, FL 33199, USA; webbj@fiu.edu;Tel.: +1-305-348-3964

Academic Editors: Jose L. Gómez, Alan P. Marscher and Svetlana G. JorstadReceived: 29 June 2016; Accepted: 17 August 2016; Published: 29 August 2016

Abstract: For the past 12 years we have been studying optical micro-variability of a sample of15 Blazars. We summarize the results of this study and draw some basic conclusions aboutthe characteristics of micro-variability. The intermittency, the stochastic nature, and the similarprofile shapes seen in micro-variations at different times and in different objects have led us toa possible model to explain the observed micro-variations. The model is based on a strong shockpropagating down a relativistic jet and encountering turbulence which causes density or magneticfield enhancements. We use the theory of Kirk, Reiger, and Mastichiadis (1998) to describe thepulse of synchrotron emission emanating from individual density enhancements energized bythe shock. By fitting these “pulses” to micro-variability observations, we obtain excellent fits toactual micro-variations. The model predicts that the spectral index changes as a function of pulseduration. This effect should be observable in multi-frequency micro-variability data. We present thetheoretical model, model fits of our micro-variability light curves, and preliminary multi-frequencymicro-variability observations that support this model. A further test that has yet to be carried outinvolves observing polarization changes in different pulses.

Keywords: Blazar; optical micro-variability; turbulent jets

1. Introduction

Micro-variations are very short-term, low amplitude variations in the optical part of the spectrum.These variations have been studied by a number of observers but no convincing model to account forthe characteristics of these variations has been found. Our program at Florida International Universityhas observed micro-variability in a sample of fifteen Blazars during 164 nights over 12 years. We definemicro-variability as: “Rapid, low-amplitude non-linear variations in optical fluctuations in a singlenight”. The observed timescales are from a few minutes to a few hours, and amplitudes range from0.01 to 0.1 magnitudes. Figure 1 is an example of micro-variations observed over a single night.

The question becomes: What are the possible explanations of this type of variability? The causecould be extrinsic to the source, perhaps by interstellar scintillation or detector effects such as driftinggains in the CCD or local weather disturbances. Interstellar scintillation can be ruled out because thetimescales are far too rapid for optical interstellar scintillation and two separate telescopes shouldobserve different waveforms [1]. Observations by Pollock, Webb and Azarnia [2] showed thattwo different telescopes at two sites with different equipment, different weather, and a differentvantage point show the same variations. This rules out both interstellar scintillation and detector/localeffects leaving intrinsic effects as the origin of these rapid variations. Since these objects are jetdominated and very little disk emission is visible during outbursts, we consider jet processes as theunderlying cause. This paper discusses the results of our program and outlines our attempts to providean explanation for the observed characteristics of these micro-variations.

Galaxies 2016, 4, 15; doi:10.3390/galaxies4030015 www.mdpi.com/journal/galaxies

Galaxies 2016, 4, 15 2 of 9

Figure 1. The micro-variability light curve of ON 231 taken over a single night.

2. Materials and Methods

The Florida International Blazar group led by the author involves observations made with the0.9 m SARA telescope at Kitt Peak National Observatory (KPNO), the 24′ ′ Lowell telescope at CerroTololo Interamerican Observatory (CTIO) and the 1 m Jacobus Kaptyne telescope at the Roque deLos Muchachos Observatory (RM). Additional observations were made with 24′ ′ telescope at theFlorida International University’s Stocker Astroscience center. The research involves students atall levels, including high school interns. This paper reports on work from a variety of students,past and present: Gopal Bhatta, now at Jagiellonian University, Douglas Laurence (current graduatestudent), Sarah Dhalla former graduate student, and undergraduates, Steven Revesz, Daniel Puentes,Carmen Vargas, Jordan DeWitt, and Archit Khanuja’Aad. High school intern Gabriel Salazar alsocontributed to data reduction. The observations made at the remote sites were done from the FIUadvanced starship telescope control room. Figure 2 shows the control room set up for remote observing.The monitors lining the front wall are all switchable; i.e., any computer output can easily be placedon the main screen or any one of the subsidiary screens. Each remote telescope has its own stationand series of monitors which monitor telescope operations, camera outputs and weather. All of thecomputers are actually located in a computer room off the main control room, and only the monitors,keyboards and mice are in the room. The room is equipped with sidereal clocks for each telescopelocation, comfortable chairs on wheels which facilitates long duration observing sessions. A completesurround sound/Blue ray entertainment system enhances the observing experience and is used forteaching and demonstrations. The furniture in the room was custom built to resemble a starship bridgeand the round room is decorated with Star Fleet command logo. It also has recessed lighting in thevertical panels and the circular ceiling to more closely resemble a starship bridge. The room has provedto be optimized and efficient for operation of up to four remote telescopes simultaneously by oneor two people. It is also large enough to accommodate up to 20 people for classroom use. The 24′ ′

computerized telescope on the roof is used for teaching and research, while the three SARA remotelyaccessible telescopes are used primarily for research.

The observations consisted of regular monitoring of several sources at a cadence of one ortwo observations per month. Objects were chosen to do micro-variability observations basedon weather, brightness, and activity considerations. We attempted to obtain a micro-variabilityobservation of each object over its entire magnitude range: faint, intermediate and flare brightness

Galaxies 2016, 4, 15 3 of 9

levels. The objects involved in this study are described in Table 1. Column 1 lists the objects commonname while column 2 shows which classification is normally associated with the source. Column 3lists the redshift while columns 4 and 5 give the number of nights observed and the number ofmicro-variability runs done for that object. The final column gives the computed duty cycle for thesource. The duty cycle is simply the time the object showed micro-variability divided by the time totaltime it was observed. Note the largest duty cycle is for S5 0716+71 at around 86%.

Figure 2. Starship control room being used to control remote telescopes at Kitt Peak NationalObservatory (KPNO) and Cerro Tololo Interamerican Observatory (CTIO).

Table 1. Source list for the FIU Blazar Micro-variability Program.

Object Type Z Nights Observed Micro-Variability Runs Duty Cycle

AO 0235+164 BL Lac 0.940 59 14 54.66PKS0420-01 OVV 0.916 31 10 15.97S5 0716+71 BL Lac 0.300 47 36 85.930735+017 BL Lac 0.424 15 2 00.000736+017 OVV 0.189 4 3 11.29

OJ 287 BL Lac 0.306 28 17 49.001156+295 OVV 0.725 16 6 50.99

3C 273 QSO 0.158 5 2 25.283C 279 OVV 0.536 28 9 61.071510-01 OVV 0.350 13 8 64.703C 345 OVV 0.593 31 12 33.83ON 231 BL Lac 0.102 4 3 29.873C 454.3 OVV 0.859 9 7 56.54BL Lac BL Lac 0.068 77 31 54.733C 446 OVV 1.404 20 4 42.86

The uneven sampling among the different sources reflects several observational biases.For historically faint sources, micro-variability observations must be done when the object is accessible,on superior photometric nights. For brighter sources, the weather requirements were less stringentand thus these sources were much more heavily observed. In addition, sources with higher dutycycles tended to be more heavily observed since we wanted to model the micro-variability inthese sources. Figure 3 shows examples of the light curves of our sources with black data points

Galaxies 2016, 4, 15 4 of 9

indicating a single night’s brightness measurement, green up-pointing arrows indicating a positivedetection of micro-variations for that night, and a down pointed red arrow indicating a negativemicro-variability detection.

Figure 3. Long-term light curves of six of the sources from Table 1. The objects are in clockwise order:AO 0235+164, S5 0716+71, PKS 1156+295, BL Lac, 3C 279, and OJ 287. Green up-pointing arrows indicatethat micro-variability was seen during that observation. The red down-pointing arrows indicated thatno micro-variability was seen during the night. Note that for these six objects in particular, the greenand red arrows both encompass the entire range of brightness and do not appear to be correlated withbrightness level.

3. Results

The results of our intensive study revealed the following observational characteristics ofmicro-variability seen in this sample. Micro-variations are intermittent, showing a large range of duty

Galaxies 2016, 4, 15 5 of 9

cycles among the different objects. The duty cycles range from 85.93 percent for BL Lac S5 0716+71to 11% for OVV quasar PKS 0736+017 (objects with more than 3 micro-variability observations).The presence of micro-variability for the objects in our sample are not statistically correlated withoverall brightness [3,4]. Analysis of our observations with time series analysis techniques such as DFT,Wavelet and periodograms do not show repeatable timescales for any of these sources [4]. Time seriesand noise analyses show that the observed micro-variations do not show characteristics of pure noisein the sense that it is unresolved red noise or white noise [5,6]. The ON 231 micro-variability curve inparticular seemed to exhibit resolved individual events or “pulses”, and this same shape seemed to bepresent in other sources as well [3,7]. We initially fit the bursts or pulses with a Gaussian curves andobtained reasonable fits, but were interested in more physical form for the pulses.

Turbulence in relativistic jets has been used to explain the lack of polarization from synchrotronemission in the jets [8,9]. Turbulence has also been proposed to account for the red noise X-ray PDSin quasars [10]. Recent models to explain the multi-wave variability in Blazars has also invoked theidea of turbulent cells in the relativistic jets [11]. We therefore proposed a model for micro-variationsin light of the intermittent nature of micro-variability and the common pulse shapes. This modelconsists of turbulence in the jet flow, the turbulent cells are then excited by shock waves and coolthrough synchrotron radiation. In this model, micro-variability is seen if the jet flow is turbulent andthe turbulent cells either pass through a standing shock or encounter a moving shock. If the jet flowis laminar, then the shock produces short-term variability but no micro-variability. The observedmicro-variability is then the superposition of individual synchrotron radiating turbulent cells. Figure 4shows a cartoon of the light curve and the possible cell distribution in the jet.

Figure 4. Illustration of a simulated micro-variability curve and a possible distribution of turbulentcells from which the light curve originates. The shock is on the left and propagates down the jet tothe right accelerating particles as it encounters the turbulent cells. The radial positions of the cells arearbitrary since we have no depth information.

We use the theory of Kirk et al. [12] to calculate the radiation emitted after the shock energizes theelectrons in the turbulent cell. Their work attempted to explain short-term flares as a planar shockencountering a dense zone in a cylindrical jet. They solve the diffusion equations:

∂n∂t− ∂

∂γ

(βsγ2n

)=

N (γ, t)tesc

δ (x− xs (t)) , (1)

I0(υ, t)=∫

dγP (ν, γ)∫

dxn(x, γ, t +xc) (2)

Galaxies 2016, 4, 15 6 of 9

These equations were programmed in IDL and solved for the frequencies at which we observeproviding pulse shapes for comparison to actual micro-variability light curves. The resulting pulseshape for a set of parameters is shown in Figure 5. The parameters used were: B = 2 Gauss,Q = 609.3 m3 s−1, ν = 4.3 × 1014, tf = 2.5/tacc, and a shock speed of 0.1c. B is the magnetic fieldin Gauss, Q is the injection rate, tf is the flare duration, and tacc is the acceleration time for electrons.Galaxies 2016, 4, 15 6 of 8

Figure 5. The pulse shape for a shock moving at 1c encountering a density enhancement with the

following parameters: B = 2 Gauss, Q = 609.3 m3 s−1, ν = 4.3 × 1014, tf = 2.5/tacc.

The maximum gamma of the electrons was assumed to be 10,000. We then scale the pulse in

amplitude and duration to fit particular pulses identified in the light curve. Figure 6 shows the

process of fitting successive pulses to a micro-variability curve.

Figure 6. The process of fitting pulses to actual micro-variability curves. The light curve is an actual

micro-variability curve of S5 0716+71 that has been linearized and fit with individual pulses. The

process is currently done by eye, but will soon be automated using a smart fitting routine.

4. Discussion

The intermittency, the stochastic nature, and the similar profile shapes seen in micro-variations

at different times and in different objects have led us to a possible model to explain the observed

behavior. The model is based on a strong shock propagating down a relativistic jet and encountering

turbulence, or conversely turbulent eddies propagating down the jet encountering a standing shock.

By fitting pulses profiles generated from the model to our micro-variability observations, we obtain

excellent fits to actual micro-variations. We fit the model to a 72-h micro-variability curve that was

the result of a WEBT collaboration [13]. Thirty-five individual pulses were fit with a correlation

coefficient of 0.97. The fits resulted in cell sizes ranging from 9 AU to 165 AU with an average of

around 33 AU. The resulting cell size distribution was a continuum up to about 60 AU consistent

with what one would expect for a stochastic process. To show this model is applicable to other sources

not on our list, the model was also fit to seven light curves of four quasars from the Kepler

observatory archives. The objects Zw 229-15, 1925+50, 1858+48, and 1904+37 all showed significant

micro-variability and the photometry was very high quality [14]. Each cadence averaged over 4200

Figure 5. The pulse shape for a shock moving at 1c encountering a density enhancement with thefollowing parameters: B = 2 Gauss, Q = 609.3 m3 s−1, ν = 4.3 × 1014, tf = 2.5/tacc.

The maximum gamma of the electrons was assumed to be 10,000. We then scale the pulse inamplitude and duration to fit particular pulses identified in the light curve. Figure 6 shows the processof fitting successive pulses to a micro-variability curve.

Figure 6. The process of fitting pulses to actual micro-variability curves. The light curve isan actual micro-variability curve of S5 0716+71 that has been linearized and fit with individual pulses.The process is currently done by eye, but will soon be automated using a smart fitting routine.

4. Discussion

The intermittency, the stochastic nature, and the similar profile shapes seen in micro-variationsat different times and in different objects have led us to a possible model to explain the observedbehavior. The model is based on a strong shock propagating down a relativistic jet and encounteringturbulence, or conversely turbulent eddies propagating down the jet encountering a standing shock.By fitting pulses profiles generated from the model to our micro-variability observations, we obtainexcellent fits to actual micro-variations. We fit the model to a 72-h micro-variability curve that was theresult of a WEBT collaboration [13]. Thirty-five individual pulses were fit with a correlation coefficient

Galaxies 2016, 4, 15 7 of 9

of 0.97. The fits resulted in cell sizes ranging from 9 AU to 165 AU with an average of around33 AU. The resulting cell size distribution was a continuum up to about 60 AU consistent with whatone would expect for a stochastic process. To show this model is applicable to other sources not on ourlist, the model was also fit to seven light curves of four quasars from the Kepler observatory archives.The objects Zw 229-15, 1925+50, 1858+48, and 1904+37 all showed significant micro-variability andthe photometry was very high quality [14]. Each cadence averaged over 4200 data points to whichan average of 42 pulses were fit. The correlation coefficients of the pulses were all over 99%. The cellsizes ranged from 10 to 140 AU for the same jet parameters and shock speeds.

A prediction of the model is that the pulses have different shapes depending on the frequenciesobserved, thus the spectral index should change as a function of pulse duration. To test the model, wehave been looking for spectral index changes as a function of pulse duration predicted by the modelusing our ground-based SARA telescopes. Figure 7 shows a micro-variability curve of 0716+71 takenon 3/4/2014 in the V and I filters. Four pulses were fit to the curve, and pulse three is shown in thebottom left panel mainly because it was the best sampled pulse of the four.

Figure 7. Simultaneous optical micro-variability light curves of S5 0716+71, taken at the SARAobservatory at Kitt Peak on 3/4/2014. The upper left curve was through a V filter, while the righttop curve was taken through an I filter. We independently fit each of the light curves to remove biasfor center times/amplitudes. We are continuing to monitor these objects to try and get additionalmulti-frequency micro-variability curves to further test the model against observations.

Future observations and tests would be better accomplished with larger aperture telescopes; thus,we are looking into proposing to spend time on large aperture instruments to follow up our one-meterobservations. A critical observation would be observing the polarization during these pulses. An idealscenario is to get an observation on a large telescope equipped with a spectra polarimeter and observe

Galaxies 2016, 4, 15 8 of 9

one of our very active Blazars. An example would be the 10.4 m GCT telescope equipped currentlywith CIRCE. With CIRCE we could measure the polarization and fluxes in the J, H and K bandssimultaneously in a single observation. This would allow not only the pulse shape with frequency testbut also the polarization changes.

5. Conclusions

The FIU/SARA Blazar micro-variability program has gathered over 12 years of observationsin several optical frequency bands. Based on the analysis of this data, we have proposed a modelfor micro-variations which successfully explains the shape of the pulses de-convolved from the lightcurves, and also the characteristic intermittency of the occurrences of micro-variations. This modelconsists of an intermittent turbulent jet with a shock wave energizing individual turbulent cells withinthe jet. The micro-variations we observe are pulses of synchrotron radiation from these coolingturbulent cells. We are testing the model with further multi-frequency micro-variability observationsand preliminary tests have been positive. We plan on using larger telescopes in the future to lookfor the subtle (∆ ~0.005 magnitude) spectral changes predicted by the model. We also discuss theuse of polarization as a further test for the model. If the model is in fact correctly interpreting themicro-variations as detecting individual turbulent cells, then cell characteristics such as the sizedistributions and perhaps Reynolds numbers of the jet flow can be gleaned from the observations andused to inform other jet calculations.

Acknowledgments: This research is based on observations obtained with the SARA Observatory 0.9 m telescopeat Kitt Peak, the 24-inch at Cerro Tololo in Chile, and the JKT at La Palma which are owned and operated by theSoutheastern Association for Research in Astronomy (saraobservatory.org).

Conflicts of Interest: The authors declare no conflict of interest.

References

1. Habibi, F.; Moniez, M.; Ansari, R.; Rahvar, S. Simulation of optical interstellar scintillation. Astron. Astrophys.2013, 552, A93. [CrossRef]

2. Pollock, J.T.; Webb, J.R.; Azarnia, G. Simultaneous Microvariability Observations of 0716+71. Astronom. J.2007, 133, 487–488. [CrossRef]

3. Howard, E.S.; Webb, J.R.; Pollock, J.T.; Stencel, R.E. Microvariability and Long-Term Variability ofFour Blazars. Astronom. J. 1994, 127, 17–23. [CrossRef]

4. Webb, J.; Bhatta, G.; Hollingsworth, H. Microvariability in Blazars. In Proceedings of the AAS 216th Meeting,Miami, FL, USA, 24–26 May 2010.

5. Dhalla, S.M.; Webb, J.R.; Bhatta, G.; Pollock, J.T. The Nature of Microvariability in Blazar 0716+71.J. Southeastern Assoc. Res. Astron. 2010, 4, 7–11.

6. Azarnia, G.; Webb, J.; Velazquez, H. FIU-NSF Correlative Study Progress Report: Microvariability andShort-Term Variations in Blazars. In Proceedings of the ASP Conference, Florida International University,Miami, Florida, USA, 10–12 April 2005.

7. Bhatta, G.; Webb, J.R.; Hollingsworth, H.; Dhalla, S.; Khanuja, A.; Bachev, R.; Blinov, D.A.; Böttcher, M.;Calle, O.B.; Calcidese, P.; et al. The 72-h WEBT Microvariability Observation of blazar S5 0716+ 714 in 2009.Astron. Astrophys. 2013, 558, A92. [CrossRef]

8. Jones, T.W. Polarization as a probe of magnetic field and plasma properties of compact radiosources-Simulation of relativistic jets. Astrophys. J. 1988, 332, 678–695. [CrossRef]

9. Marscher, A.P.; Gear, W.K.; Travis, J.P. Variability of Nonthermal Continuum Emission in Blazars. In Variabilityof Blazars; Valtaoja, E., Valtonen, M., Eds.; Cambridge University Press: Cambridge, UK, 1992; p. 85.

10. McHardy, I. Explaining X-ray Variability in Blazars. In Proceedings of the Workshop on Blazar Variabilityacross the EM Spectrum, Palaiseau, France, 22–25 April 2008.

11. Marscher, A.P.; Jorstad, S.G. Rapid Variability of Gamma-ray Emission from Sites near the 43 GHz Cores ofBlazar Jets. 2010, arXiv:1005.5551v1.

12. Kirk, J.G.; Rieger, F.M.; Mastichiadis, A. Particle acceleration and synchrotron emission in blazar jets.Astron. Astrophys. 1998, 333, 452–458.

Galaxies 2016, 4, 15 9 of 9

13. Bhatta, G.; Webb, J.R.; Hollingsworth, H.; Dhalla, S.; Khanuja, A.; Bachev, R.; Blinov, D.A.; Böttcher, M.;Calle, O.B.; Calcidese, P.; et al. The 72-h WEBT microvariability observation of blazar S5 0716+714 in 2009.Astron. Astrophys. 2013, 558, A92. [CrossRef]

14. Bhatta, G.; Webb, J.; Dhalla, S.; Hallingsworth, H. A Model for Microvariability in Blazars. In Proceedings ofthe AAS 218th Meeting, Boston, MA, USA, 22–26 May 2011.

© 2016 by the author; licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC-BY) license (http://creativecommons.org/licenses/by/4.0/).